Abstract

Text Categorization is a task of assigning documents to a fixed number of predefined categories. Concept is the grouping of semantically related items under a unique name. Dimensionality space and sparsity of the document representation can be reduced using concept generation. Conceptual representation of a text can be generated using WordNet. In this paper, an empirical evolution using Convolutional Neural Networks (CNN) for text categorization has been performed. The Convolutional Neural Networks exploit the one-dimensional structures of the text such as words, concepts, word embeddings, and concept embeddings to improve the categorical label prediction. The Reuter’s dataset is evaluated with Convolutional Neural Networks on four categories of data. The representation of a text with word embeddings and concept embeddings together results to a better classification performance using CNN compared with word embeddings and concept embeddings individually.